Interpretive Summary: There is a concept that the well-being of an animal is all about the animal's emotional state and whether it is suffering, i.e. "welfare is dependent on what animals feel". Animal suffering is extremely difficult to address but remains central to the concept of animal welfare for many scientists and is possibly the most important factor when the general public form opinions about farming systems. Scientific advances in the areas of neuroscience, psychophysiology and testing of behavioural needs begin to demonstrate that perhaps the measurement or quantification of emotional states can be attainable. One such area is that of heart rate variability (HRV) which is widely used in clinical medicine, where it is a useful tool in the diagnosis of a number of non-cardiac disease states and in the investigation of cardiac disease. HRV analysis may also yield information on other factors important in well-being, namely pain and emotional state. Progress in the development of methodologies suitable for use in farm animals has been slow. One contributory reason for this is the limited availability of affordable equipment capable of non-invasively monitoring and storing large amounts of heart rate data in unrestrained animals. The objectives of this present study were to validate a new piece of equipment designed for use in humans, for use in pigs, and to establish a standard method of editing the data so that they are suitable for HRV analysis. We carried out an experiment on 5 gilts using both the new Polar RR Recorder and a telemetric ECG system. Data from both systems were then compared and, using the ECG data as gold standard, the Polar data were examined, errors identified and edited. HRV analysis showed that the uncorrected Polar data would give very different results from the ECG data. However, using the methodology derived by us, the corrected Polar data could be used interchangeably with the ECG data. Thus, we have now validated the relatively inexpensive Polar equipment for use in pigs and established what should become a standardized method for data handling and editing. Our findings will have a large impact for fellow researchers using HRV analysis in animal well-being research, and should enable us to increase our fundamental knowledge of how animal's respond to stressful situations over both the short- and long-term.

Technical Abstract:
Autonomic regulation of cardiac activity during stress has not been clearly defined in farm animals. In part, this is due to the limited availability of affordable ambulatory cardiac monitors capable of accurately monitoring and storing large amounts of data that meet the criteria necessary for heart rate variability analysis. Our objectives were; to measure the accuracy of a 24 hr Polar RR monitor using gold standard ECG, to examine and categorise any occurring anomalies, and to ascertain their impact on the outcome of heart rate variability analysis. Five one-year-old female pigs (gilts) were socially isolated from their pen mates and cardiac activity was simultaneously measured using 2 systems, a 24-hour Polar RR Recorder and a Telemetric ECG system. The Polar data were manually assessed both against and in isolation of the ECG data to identify anomalous beats, which were then assigned to one of 5 identified error categories. The anomalies in the Polar data were corrected and statistical comparisons were performed between the 3 data sets, using repeated measures GLM and contrast comparisons, to evaluate the effects of anomalies on heart rate variability analysis. Bland-Altman analysis was used to measure the level of agreement between the ECG, Uncorrected Polar, and Corrected Polar data. No anomalies or ectopies were found in the ECG data but 46 anomalies (0.81% of total IBIs) were found in the Polar Uncorrected data. Manual identification and editing procedures reduced this error to 0.018%. Most mean HR and IBI parameters were unaffected by error (P>0.05). Variance and root mean square of successive inter beat intervals (RMSSD) were 45 and 50% higher when anomalies were present in the data. Artefacts affected the magnitude of the frequency domain indices and overestimated total and parasympathetic activity and underestimated sympathetic activity. The mean difference between the ECG and Uncorrected Polar data was 1.36ms (limits of agreement -69.03 to 71.74 ms). This was greatly improved to 0.36 ms (limits of agreement -5.37 to 6.10 ms) after editing. Overall, even a small proportion of error biased the outcome of heart rate variability analysis. This bias was greatly reduced by correcting the anomalous beats. Bland-Altman analysis demonstrated that when there was error present in the Polar data it could not be used interchangeably with the ECG data. However, if there were no anomalies present in the data or if they were classified and corrected using the approach in this study then the two systems could be used interchangeably.